This project aims to develops a completely manufacturing flexible decision framework by applying big data analytics and AI techniques that integrates data from different decision-making units such as APC, APS, capacity planning, and inventory management planning to increase the decision quality and resilient capability in uncertain risks based on the perspective of factory operation.

We have proposed in this project the first time series classification technique that considers accuracy, earliness, and varied lengths simultaneously, containing a novel deep reinforcement learning framework and a new multi-objective optimization neural network algorithm. The proposed technique is fit for the problem of early classification of cardiovascular diseases based on ECG signals and shown to deliver the best performance in this area, holding the leading position worldwide.

A self-improving thermal error compensation model is established by federated learning to improve the machine tool performance under different ambient temperatures. Each machine can share and update the latest model parameter to improve the initial model from different ambient temperature variation trails, but still keep the private ambient information of each machine.

Develop manufacturing technology and value for difficult-to-machine superalloy materials, fully automated machining technology, improve efficiency and machining quality stability, and combine automation and intelligence for unmanned autonomous operation of machining and inspection.

Multidisciplinary rounds (MDRs) are scheduled, patient-focused communication mechanisms among multidisciplinary providers in the intensive care unit (ICU). The surgical ICU team of National Cheng Kung University Hospital has developed and integrated i-Dashboard as a platform to facilitate MDRs. i-Dashboard is a custom-developed visualization dashboard that supports (1) key information retrieval and reorganization, (2) time-series data, and (3) display on large touchscreens during MDRs. The i-Dashboard increases the efficiency in data gathering and enhances communication accuracy and information exchange in MDRs.

The NeRF-based technique describes a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene with known poses.

By using the machine learning algorithms, this study developed a risk stratification model for predicting the occurrence of in-hospital cardiac arrest (IHCA) events in patients admitted from the emergency department with COVID-19 and pneumonia. The results showed that the model's performance is better than by using the National Early Warning Score (NEWS).

Our team develops an accurate PWV estimation algorithm that uses wrist PPG and ECG signals from wearable devices. A missing-feature imputation and ambiguous-feature resolution technique is developed and the availability of wrist PPG morphological features is raised from 60% to 99.1%. A weighted pulse decomposition approach is adopted and 5 component waves can be acquired to examine more detailed properties. The PWV is then estimated by XGBoost algorithm with the hierarchical regression model.

The Pericardium/Aorta Segmentation and Cardiovascular Risk Prediction AI Total Solution Model, HeaortaNet, is a deep learning model based on UNet and attention gate, and had been trained by >70,000 axial images with verified annotations of the pericardium and aorta. It shortens the time for data processing from 60 minutes, by manual segmentation of both pericardium and aorta, to 0.4 seconds. The segmentation accuracy is 94.8% for the pericardium, and 91.6% for the aorta. The applicability of HeaortaNet had been demonstrated by analyzing the non-contrast chest CT scans (>5,000 cases) deposited in the mega-image bank of National Health Insurance Databank.

YOLOv7 is a new generation of real-time object detector, which provides the most advanced real-time object detection architecture from edge computing to cloud computing.